Poster Presentation Australian & New Zealand Obesity Society 2014 Annual Scientific Meeting

The clinical obesity maintenance model - an evaluation (#173)

Jayanthi Raman 1 , Phillipa Hay 2 , Evelyn Smith 3 4
  1. School of Medicine, University of Western Sydney, Sydney, NSW, Australia
  2. Centre for Health Research, School of Medicine, University of Western Sydney, Sydney, NSW, Australia
  3. Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia
  4. School of Psychiatry, University of New South Wales, Randwick, NSW, Australia

We (Raman et al. 2013) have developed a conceptual and theoretical framework of obesity maintenance termed the Clinical Obesity Maintenance Model (COMM). We argue that psychological variables, that of executive function, habit clusters, emotional dysregulation, level of depressed mood, and health literacy (eating beliefs and attitudes about obesity) interact and impact on the overeating/binge eating and sedentary obesity maintaining behaviours of individuals who are obese. The main aim of the present study was to test how well the COMM predicts obesity maintaining behaviours in an adult community sample. Specific aims were: (a) to test the strength and direction of associations between the individual constructs of the model, and (b) the independent effects of individual model constructs on obesity maintaining behaviours. Study participants (n=100) were community volunteers who completed the following assessments and instruments: a demographic and anthropometric profile, a neuropsychological battery that included tests of executive function (e.g. ,Wisconsin Card Sort Test, Rey Complex Figure Test, Brief A, Trail Making Task and Digit Span), the Self Report Habit Index, the Depression Anxiety and Stress Scale, Difficulties in Emotion Regulation Scale, Modified Health Literacy Questionnaire and the Eating Beliefs, GrazingĀ  and the Eating Disorder Examination Questionnaires. Data were analysed with univariate correlational statistics followed by multivariate statistical modelling using multiple regression.